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Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git

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Issues 62 Pull Requests 44 Discussions 0

#609 Ci fix

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Ghost merged 1 commits into Deci-AI:master from deci-ai:bugfix/infra-000_ci
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super-gradients
/
src
/
super_gradients
/
training
/
pretrained_models.py
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  1. MODEL_URLS = {
    2. 

  2.     # RegNet-s
    3. 

  3.     "regnetY800_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY800/average_model.pth",
    4. 

  4.     "regnetY600_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY600/average_model_regnety600.pth",
    5. 

  5.     "regnetY400_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY400/average_model_regnety400.pth",
    6. 

  6.     "regnetY200_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY200/average_model_regnety200.pth",
    7. 

  7.     # ResNet-s
    8. 

  8.     "resnet50_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/KD_ResNet50_Beit_Base_ImageNet/resnet.pth",
    9. 

  9.     "resnet34_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/resent_34/average_model.pth",
    10. 

  10.     "resnet18_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/resnet18/average_model.pth",
    11. 

  11.     #
    12. 

  12.     "repvgg_a0_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/repvgg_a0_imagenet.pth",
    13. 

  13.     #
    14. 

  14.     "shelfnet34_lw_coco_segmentation_subclass": "https://deci-pretrained-models.s3.amazonaws.com" "/shelfnet34_coco_segmentation_subclass.pth",
    15. 

  15.     #
    16. 

  16.     "ddrnet_23_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ddrnet/cityscapes/ddrnet23/average_model.pth",
    17. 

  17.     "ddrnet_23_slim_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ddrnet/cityscapes/ddrnet23_slim/average_model.pth",
    18. 

  18.     #
    19. 

  19.     "stdc1_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/cityscapes_stdc1_seg50_dice_edge/ckpt_best.pth",
    20. 

  20.     "stdc1_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/stdc1_seg75_cityscapes/ckpt_best.pth",
    21. 

  21.     "stdc2_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/cityscapes_stdc2_seg50_dice_edge/ckpt_best.pth",
    22. 

  22.     "stdc2_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/stdc2_seg75_cityscapes/ckpt_best.pth",
    23. 

  23.     #
    24. 

  24.     "efficientnet_b0_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/efficientnet_b0/average_model-3.pth",
    25. 

  25.     #
    26. 

  26.     "ssd_lite_mobilenet_v2_coco": "https://deci-pretrained-models.s3.amazonaws.com/ssd_lite_mobilenet_v2/coco2017/2022-11-28/average_model.pth",
    27. 

  27.     "ssd_mobilenet_v1_coco": "https://deci-pretrained-models.s3.amazonaws.com/ssd_mobilenet_v1_coco_res320/ckpt_best.pth",
    28. 

  28.     #
    29. 

  29.     "mobilenet_v3_large_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/mobilenetv3+large+300epoch/average_model.pth",
    30. 

  30.     "mobilenet_v3_small_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/mobilenetv3+small/ckpt_best.pth",
    31. 

  31.     "mobilenet_v2_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/mobilenetv2+w1/ckpt_best.pth",
    32. 

  32.     #
    33. 

  33.     "regseg48_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/regseg48_cityscapes/ckpt_best.pth",
    34. 

  34.     #
    35. 

  35.     "vit_base_imagenet21k": "https://deci-pretrained-models.s3.amazonaws.com/vit_pretrained_imagenet21k/vit_base_16_imagenet21K.pth",
    36. 

  36.     "vit_large_imagenet21k": "https://deci-pretrained-models.s3.amazonaws.com/vit_pretrained_imagenet21k/vit_large_16_imagenet21K.pth",
    37. 

  37.     "vit_base_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/vit_base_imagenet1k/ckpt_best.pth",
    38. 

  38.     "vit_large_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/vit_large_cutmix_randaug_v2_lr%3D0.03/average_model.pth",
    39. 

  39.     #
    40. 

  40.     "beit_base_patch16_224_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/beit_base_patch16_224_imagenet.pth",
    41. 

  41.     "beit_base_patch16_224_cifar10": "https://deci-pretrained-models.s3.amazonaws.com/beit_cifar10.pth",
    42. 

  42.     #
    43. 

  43.     "yolox_s_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_s_coco/average_model.pth",
    44. 

  44.     "yolox_m_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_m_coco/average_model.pth",
    45. 

  45.     "yolox_l_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_l_coco/average_model.pth",
    46. 

  46.     "yolox_t_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_tiny_coco/ckpt_best.pth",
    47. 

  47.     "yolox_n_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_n_coco/ckpt_best.pth",
    48. 

  48.     #
    49. 

  49.     "pp_lite_t_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_t_seg50/average_model.pth",
    50. 

  50.     "pp_lite_t_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_t_seg75/average_model.pth",
    51. 

  51.     "pp_lite_b_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_b_seg50/average_model.pth",
    52. 

  52.     "pp_lite_b_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_b_seg75/average_model.pth",
    53. 

  53. }
    54. 

  54. 

  55. PRETRAINED_NUM_CLASSES = {"imagenet": 1000, "imagenet21k": 21843, "coco_segmentation_subclass": 21, "cityscapes": 19, "coco": 80, "cifar10": 10}
    56.
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